Natural Language-conditioned reinforcement learning (RL) enables the agents
to follow human instructions. Previous approaches generally implemented
language-conditioned RL by providing human instructions in natural language
(NL) and training a following policy. In this outside-in approach, the policy
needs to comprehend the NL and manage the task simultaneously. However, the
unbounded NL examples often bring much extra complexity for solving concrete RL
tasks, which can distract policy learning from completing the task. To ease the
learning burden of the policy, we investigate an inside-out scheme for natural
language-conditioned RL by developing a task language (TL) that is task-related
and unique. The TL is used in RL to achieve highly efficient and effective
policy training. Besides, a translator is trained to translate NL into TL. We
implement this scheme as TALAR (TAsk Language with predicAte Representation)
that learns multiple predicates to model object relationships as the TL.
Experiments indicate that TALAR not only better comprehends NL instructions but
also leads to a better instruction-following policy that improves 13.4% success
rate and adapts to unseen expressions of NL instruction. The TL can also be an
effective task abstraction, naturally compatible with hierarchical RL.

采用内部 - 外部模式的自然语言控制强化学习方法使用任务语言与谓词表示来学习多个断言以模拟对象关系，并将翻译器用于自然语言到任务语言的转换，有效地提高 13.4% 的成功率和适应性，并成为有效的任务抽象形式，与层次强化学习相容。

内部 - 外显任务语言发展与翻译驱动的自然语言指导下的强化学习

Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation

Neural machine translation is a relatively new approach to statistical
machine translation based purely on neural networks. The neural machine
translation models often consist of an encoder and a decoder. The encoder
extracts a fixed-length representation from a variable-length input sentence,
and the decoder generates a correct translation from this representation. In
this paper, we focus on analyzing the properties of the neural machine
translation using two models; RNN Encoder--Decoder and a newly proposed gated
recursive convolutional neural network. We show that the neural machine
translation performs relatively well on short sentences without unknown words,
but its performance degrades rapidly as the length of the sentence and the
number of unknown words increase. Furthermore, we find that the proposed gated
recursive convolutional network learns a grammatical structure of a sentence
automatically.

分析神经机器翻译的性能，研究了两种模型：RNN 编码器 - 解码器和新提出的门控递归卷积神经网络，结果显示神经机器翻译在翻译较短，不含生词句子时表现相对良好，但随着句子长度和生词数的增加，其性能会迅速降低。同时发现，所提出的门控递归卷积网络能够自动学习句子的语法结构。